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Supply Chain & Procurement · Engineering
Data & Classical Analytics / Mathematical Optimization
Make-vs-buy and supplier allocation optimization
Scaling Adjacent medium effect
Core capability
The system computes the best feasible production or logistics plan under real constraints, helping improve throughput, delivery reliability, and cost efficiency.
How it works
Business rules, capacities, deadlines, and resource limits are encoded mathematically, and the solver computes the best feasible plan instead of leaving planners to resolve trade-offs manually.
Application here
Optimization models evaluate make-vs-buy options, supplier selection, and capacity allocation under cost and capacity constraints.
Business impact
This improves sourcing decisions by replacing spreadsheet-style comparisons with more systematic multi-factor optimization.
Limitations
The models miss strategic factors such as supplier relationships and geopolitical risk, and results depend heavily on input-data accuracy.
In production
This is already a real production capability in many companies: the system helps build better plans for production, logistics, and resources under real business limits.
Research
The direction of travel is toward systems where a planner describes the problem in business language and the software helps turn that into a solvable planning model much faster than today.
Examples
Air France Flight Schedule Optimization
Description: Air France applied mathematical optimization to its fleet scheduling, minimizing fuel costs under constraints like crew hours, gate availability, and aircraft capacity, achieving 1% annual fuel savings worth millions of dollars.



Mondelez International Shipment Scheduling
Description: Mondelez used mathematical optimization for product shipment planning, speeding up schedule generation by 92% while optimizing under production and logistics constraints for better throughput and reliability.



New York ISO Power Distribution Optimization
Description: The New York ISO employs mathematical optimization to select cost-effective electricity provision methods, balancing supply constraints, demand, and costs across the grid for efficient resource allocation.
https://hbr.org/sponsored/2024/09/using-mathematical-optimization-to-solve-complex-business-challengeshttps://www.dataversity.net/articles/making-business-decisions-with-mathematical-optimization/
Sources
Gurobi — Boeing case study — ; IBM Decision Optimization —
https://www.gurobi.com/case_studies/boeing/https://www.ibm.com/products/ilog-cplex-optimization-studio